for scale_covar in (True, False):
myfit.scale_covar = scale_covar
print ' ==== scale_covar = ', myfit.scale_covar, ' ==='
for sigma in (0.1, 0.2, 0.23, 0.5):
myfit.userkws['sigma'] = sigma
p_fit['amp_g'].value = 10
p_fit['cen_g'].value = 9
p_fit['wid_g'].value = 1
p_fit['line_slope'].value =0.0
p_fit['line_off'].value =0.0
myfit.leastsq()
print ' sigma = ', sigma
print ' chisqr = ', myfit.chisqr
print ' reduced_chisqr = ', myfit.redchi
report_errors(p_fit, modelpars=p_true, show_correl=False)
print ' =============================='
# if HASPYLAB:
# fit = residual(p_fit, x)
# pylab.plot(x, fit, 'k-')
# pylab.show()
#
xmax = 250.0
noise = random.normal(scale=0.7215, size=n)
x = linspace(xmin, xmax, n)
data = residual(p_true, x) + noise
fit_params = Parameters()
fit_params.add('amp', value=13.0)
fit_params.add('period', value=2)
fit_params.add('shift', value=0.0)
fit_params.add('decay', value=0.02)
out = minimize(residual, fit_params, args=(x,), kws={'data':data})
fit = residual(fit_params, x)
print ' N fev = ', out.nfev
print out.chisqr, out.redchi, out.nfree
report_errors(fit_params)
if HASPYLAB:
pylab.plot(x, data, 'ro')
pylab.plot(x, fit, 'b')
pylab.show()
("a1", 8.0, True, None, 14.0, None),
("c1", 5.0, True, None, None, None),
("w1", 0.7, True, None, None, None),
("a2", 3.1, True, None, None, None),
("c2", 8.8, True, None, None, None),
)
fit_params.add("w2", expr="2.5*w1")
myfit = Minimizer(residual, fit_params, fcn_args=(x,), fcn_kws={"data": data})
myfit.prepare_fit()
init = residual(fit_params, x)
if HASPYLAB:
pylab.plot(x, init, "b--")
myfit.leastsq()
print " N fev = ", myfit.nfev
print myfit.chisqr, myfit.redchi, myfit.nfree
report_errors(fit_params)
fit = residual(fit_params, x)
if HASPYLAB:
pylab.plot(x, fit, "k-")
pylab.show()
sigma = 0.021 # estimate of data error (for all data points)
myfit = Minimizer(residual,
pfit,
iter_cb=per_iteration,
fcn_args=(x, ),
fcn_kws={
'sigma': sigma,
'data': data
},
scale_covar=True)
myfit.prepare_fit()
init = residual(myfit.params, x)
if HASPYLAB:
pylab.plot(x, init, 'b--')
myfit.leastsq()
print(' Nfev = ', myfit.nfev)
print(myfit.chisqr, myfit.redchi, myfit.nfree)
report_errors(myfit.params, modelpars=p_true)
fit = residual(myfit.params, x)
if HASPYLAB:
pylab.plot(x, fit, 'k-')
pylab.show()
sigma = 0.021 # estimate of data error (for all data points)
myfit = Minimizer(residual, pfit, iter_cb=per_iteration,
fcn_args=(x,), fcn_kws={'sigma':sigma, 'data':data},
scale_covar=True)
myfit.prepare_fit()
init = residual(myfit.params, x)
if HASPYLAB:
pylab.plot(x, init, 'b--')
myfit.leastsq()
print(' Nfev = ', myfit.nfev)
print( myfit.chisqr, myfit.redchi, myfit.nfree)
report_errors(myfit.params, modelpars=p_true)
fit = residual(myfit.params, x)
if HASPYLAB:
pylab.plot(x, fit, 'k-')
pylab.show()
p_fit.add('wid_l', expr='wid_g')
p_fit.add('line_slope', value=0.0)
p_fit.add('line_off', value=0.0)
myfit = Minimizer(residual, p_fit,
fcn_args=(x,), fcn_kws={'data':data})
myfit.prepare_fit()
init = residual(p_fit, x)
if sys.version_info[0] == 2:
pylab.plot(x, init, 'b--')
myfit.leastsq()
print(' Nfev = ', myfit.nfev)
print( myfit.chisqr, myfit.redchi, myfit.nfree)
report_errors(p_fit, modelpars=p_true)
fit = residual(p_fit, x)
if sys.version_info[0] == 2:
pylab.plot(x, fit, 'k-')
pylab.show()
'data': data
})
myfit.prepare_fit()
#
for scale_covar in (True, False):
myfit.scale_covar = scale_covar
print ' ==== scale_covar = ', myfit.scale_covar, ' ==='
for sigma in (0.1, 0.2, 0.23, 0.5):
myfit.userkws['sigma'] = sigma
p_fit['amp_g'].value = 10
p_fit['cen_g'].value = 9
p_fit['wid_g'].value = 1
p_fit['line_slope'].value = 0.0
p_fit['line_off'].value = 0.0
myfit.leastsq()
print ' sigma = ', sigma
print ' chisqr = ', myfit.chisqr
print ' reduced_chisqr = ', myfit.redchi
report_errors(p_fit, modelpars=p_true, show_correl=False)
print ' =============================='
# if HASPYLAB:
# fit = residual(p_fit, x)
# pylab.plot(x, fit, 'k-')
# pylab.show()
#
